Modelo matemático de planificación de rutas para minimizar los costos del reparto de la empresa San Isidro Labrador S.R.L. en el año 2015
Bibliographic record
Abstract
La presente tesis busco planificar las rutas de reparto de carga a traves de un modelo matematico para minimizar los costos del reparto de cargas de la empresa San Isidro Labrador S.R.L. en el ano 2015. El estudio se aplico a los 275 principales clientes de esta empresa, de los cuales se escogio por muestreo de poblaciones finitas a 161 clientes, realizandose un estudio pre test y pos test, a quienes se aplico un cuestionario que mide la satisfaccion de la calidad del servicio de reparto, luego se procedio mapear a los 45 clientes insatisfechos en Google MAPS y medir las distancias entre nodos obteniendo la zonificacion de 5 clusters por cercania de puntos, seguido se calculo los costos operativos por hora de mano de obra, mantenimiento y combustible y se desarrollo el modelo matematico de algoritmo de petalos en LINGO System siendo la funcion objetivo minimizar los costos del reparto de carga y las restricciones de demanda, capacidad, tiempo total, hora de salida y kilometraje del vehiculo. Teniendo como resultados una reduccion del 43.7% los costos de reparto y un 49.9% de distancia recorrida. El impacto del modelo matematico en los costos del reparto fueron corroborados con la prueba estadistica t-student, dando un valor (p=0.017) menor que 0.05. Lo cual permitio aceptar la hipotesis del modelo matematico de planificacion de rutas si minimiza los costos del reparto de carga.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".